Lens
Quality Control and Predictive Maintenance Management
See defects before they ship.
Detect quality drift before it ships. Predict maintenance windows before they become unplanned downtime. Same data pipeline as Solution 1 — different optimization surface. IoT sensor integration where the value justifies it, optional everywhere else.
What you get
The trust moat for industrial AI buyers.
Solution 2 is KPT's answer to the #1 fear in industrial AI: what if it makes things worse? Every Optimization in this Solution layers on top of the 30-day shadow-run trust gate — every variable, every model output, every recommendation is A/B-tested against your real data with statistical significance before promotion.
Three flagship Optimizations ship together:
- Vibration-Anomaly Predictor — IoT vibration sensors on rotating equipment (mills, motors, pumps, conveyors) predict failure 7–14 days ahead.
- In-Line Statistical Process Control — SPC on critical-to-quality parameters with auto-alerts on drift. No more "we only catch it at QC inspection."
- Predictive Spare-Parts Stockout — paired with vibration prediction; minimize MRO inventory without ever blowing a stockout.
Lens roadmap
5 Optimizations. 0 live, 3 in development, 2 on the roadmap.
Each Solution ships as a sequence of bounded Optimizations. The live ones can be adopted today; the in-development ones are funded and scheduled; the roadmap ones are scoped, not yet committed. Customers can join at any phase — pilot what's live, co-fund what's in development, or shape what's still on the board.
Available now
0In development
3-
Vibration-Anomaly Predictor
IoT-fed vibration analytics that flag bearing/motor degradation 7–14 days before unplanned failure. The flagship Lens Optimization.
- Effort
- L
- Impact
- XL
- Target
- 2026 Q2
-
In-Line Statistical Process Control
Real-time SPC over in-line quality measurements with auto-tightening control limits when process drift is detected. Catches defects before they batch.
- Effort
- M
- Impact
- L
- Target
- 2026 Q2
-
Predictive Spare-Parts Stockout
Forecast which spare parts will be needed in the next 30/60/90 days based on equipment age, duty cycle, and historical failure patterns.
- Effort
- M
- Impact
- M
- Target
- 2026 Q3
On the roadmap
2-
Quality-Drift Early Detection
Multivariate drift detection across quality dimensions — catches subtle process changes that cause cumulative quality erosion before they reach SPC limits.
- Effort
- L
- Impact
- L
- Planned
- 2027 Q1
-
Maintenance Window Optimizer
Schedule preventive maintenance into production windows where opportunity cost is lowest, without violating equipment-vendor service intervals.
- Effort
- M
- Impact
- M
- Planned
- 2027 Q2
Effort and Impact are estimated on a S / M / L / XL scale (1 dot to 4 dots). Effort = engineering work required to ship; Impact = expected operational improvement at typical industrial scale. Estimates are KPT internal benchmarks and are validated against your data during the 30-day shadow-run PoC before any commitment to scale.
Where this Solution lands
Industries we've prototyped this for
Mining
SAG mills, crushers, conveyors — predictive vibration catches failures 7–14 days out.
Vale — pipeline
See Mining →
Pulp & Paper
Refiners, motors, pumps health + paper-machine SPC for basis weight + brightness drift.
See Pulp & Paper →
Beverage
Fillers, carbonators, compressors — high-velocity equipment with high replacement cost.
See Beverage →
Confectionery
Mixers, extruders, molders — quality SPC on weight, sugar content, shape.
See Confectionery →
Want early access?
Join the Solution 2 pre-launch cohort.
We're building Solution 2 with three reference customers ahead of June 2026 launch. Early participants shape the priors, get retrofit-cost-neutral IoT installs, and lock first-mover pricing. Three slots, two open.